Usewhisper Autohook

usewhisper-autohook (OpenClaw Skill)

This skill is a thin wrapper designed to make "automatic memory" easy:

• get_whisper_context(user_id, session_id, current_query) for pre-response context injection
• ingest_whisper_turn(user_id, session_id, user_msg, assistant_msg) for post-response ingestion

It defaults to the token-saving settings you almost always want:

• compress: true
• compression_strategy: "delta"
• use_cache: true
• include_memories: true

It also persists the last context_hash locally (per api_url + project + user_id + session_id) so delta compression works by default without you needing to pass previous_context_hash.

Install (ClawHub)

npx clawhub@latest install usewhisper-autohook

Setup

Set env vars wherever OpenClaw runs your agent:

WHISPER_CONTEXT_API_URL=https://context.usewhisper.dev
WHISPER_CONTEXT_API_KEY=YOUR_KEY
WHISPER_CONTEXT_PROJECT=openclaw-yourname

Notes:

• WHISPER_CONTEXT_API_URL is optional (defaults to https://context.usewhisper.dev).
• The helper will auto-create the project on first use if it does not exist yet.

The "Auto Loop" Prompt (Copy/Paste)

Add this to your agent's system instruction (or equivalent):

Before you think or respond to any message:
1) Call get_whisper_context with:
   user_id = "telegram:{from_id}"
   session_id = "telegram:{chat_id}"
   current_query = the user's message text
2) If the returned context is not empty, prepend it to your prompt as:
   "Relevant long-term memory:\n{context}\n\nNow respond to:\n{user_message}"

After you generate your final response:
1) Call ingest_whisper_turn with the same user_id and session_id and:
   user_msg = the full user message
   assistant_msg = your full final reply

Always do this. Never skip.

If you are not on Telegram, keep the same structure: the important part is that user_id and session_id are stable.

If Your Agent Still Replays Full Chat History (Proxy Mode)

If you cannot control how your agent/framework constructs prompts (it always sends the full conversation history), a system prompt cannot reduce token spend: the tokens are already sent to the model.

In that case, run the built-in OpenAI-compatible proxy so the network payload is actually reduced. The proxy:

• receives POST /v1/chat/completions
• queries Whisper memory
• strips chat history down to system + last user message
• injects Relevant long-term memory: ...
• calls your upstream OpenAI-compatible provider
• ingests the turn back into Whisper

Start the proxy:

export OPENAI_API_KEY="YOUR_UPSTREAM_KEY"
node usewhisper-autohook.mjs serve_openai_proxy --port 8787

Then point your agent’s OpenAI base URL to http://127.0.0.1:8787 (exact env/config depends on your agent).

If your agent supports overriding the upstream base URL, you can set:

• OPENAI_BASE_URL (for OpenAI-compatible upstreams)
• ANTHROPIC_BASE_URL (for Anthropic upstreams)

Or pass --upstream_base_url when starting the proxy.

For correct per-user/session memory, pass headers on each request:

• x-whisper-user-id: telegram:{from_id}
• x-whisper-session-id: telegram:{chat_id}

Anthropic Native Proxy (/v1/messages)

If your agent uses Anthropic's native API (not OpenAI-compatible), run the Anthropic proxy instead:

export ANTHROPIC_API_KEY="YOUR_ANTHROPIC_KEY"
node usewhisper-autohook.mjs serve_anthropic_proxy --port 8788

Then point your agent’s Anthropic base URL to http://127.0.0.1:8788.

Pass IDs via headers (recommended):

• x-whisper-user-id: telegram:{from_id}
• x-whisper-session-id: telegram:{chat_id}

If you do not pass headers, the proxies will attempt to infer stable IDs from OpenClaw's system prompt / session key if present. This is best-effort; headers are still the most reliable.

CLI Usage (what the tools call)

All commands print JSON to stdout.

Get packed context

node usewhisper-autohook.mjs get_whisper_context \
  --current_query "What did we decide last time?" \
  --user_id "telegram:123" \
  --session_id "telegram:456"

Ingest a completed turn

node usewhisper-autohook.mjs ingest_whisper_turn \
  --user_id "telegram:123" \
  --session_id "telegram:456" \
  --user_msg "..." \
  --assistant_msg "..."

For large content, pass JSON via stdin:

echo '{ "user_msg": "....", "assistant_msg": "...." }' | node usewhisper-autohook.mjs ingest_whisper_turn --session_id "telegram:456" --user_id "telegram:123" --turn_json -

Output Format

get_whisper_context returns:

• context: the packed context string to prepend
• context_hash: a short hash you can store and pass back as previous_context_hash next time (optional)
• meta: cache hit and compression info (useful for debugging)